Articles | Volume 26, issue 8
https://doi.org/10.5194/hess-26-2245-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-2245-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 May 2022
Research article |
| 02 May 2022
| 02 May 2022
The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Xingyuan Chen
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Utkarsh Mital
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
Ethan T. Coon
Climate Change Science Institute & Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Dipankar Dwivedi
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
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We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Maggi M. Laan, Stephanie G. Fulton, Vanessa A. Garayburu-Caruso, Morgan E. Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Amy E. Goldman, Samantha Grieger, Robert O. Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin L. M. Zionce, Sophia A. McKever, Allison Myers-Pigg, Opal Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1109, https://doi.org/10.5194/egusphere-2025-1109, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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Respiration is a process that combines carbon and oxygen to generate energy for living organisms. Within a river, respiration in sediments and water have variable contributions to respiration of the whole river system. Contrary to conventional wisdom, we found that water column respiration did not increase systematically moving from small streams to big rivers. Instead, it was locally influenced by temperature, nutrients and suspended solids.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
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We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
James Stegen, Amy J. Burgin, Michelle H. Busch, Joshua B. Fisher, Joshua Ladau, Jenna Abrahamson, Lauren Kinsman-Costello, Li Li, Xingyuan Chen, Thibault Datry, Nate McDowell, Corianne Tatariw, Anna Braswell, Jillian M. Deines, Julia A. Guimond, Peter Regier, Kenton Rod, Edward K. P. Bam, Etienne Fluet-Chouinard, Inke Forbrich, Kristin L. Jaeger, Teri O'Meara, Tim Scheibe, Erin Seybold, Jon N. Sweetman, Jianqiu Zheng, Daniel C. Allen, Elizabeth Herndon, Beth A. Middleton, Scott Painter, Kevin Roche, Julianne Scamardo, Ross Vander Vorste, Kristin Boye, Ellen Wohl, Margaret Zimmer, Kelly Hondula, Maggi Laan, Anna Marshall, and Kaizad F. Patel
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The loss and gain of surface water (variable inundation) are common processes across Earth. Global change shifts variable inundation dynamics, highlighting a need for unified understanding that transcends individual variably inundated ecosystems (VIEs). We review the literature, highlight challenges, and emphasize opportunities to generate transferable knowledge by viewing VIEs through a common lens. We aim to inspire the emergence of a cross-VIE community based on a proposed continuum approach.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Mingjie Shi, Nate McDowell, Huilin Huang, Faria Zahura, Lingcheng Li, and Xingyuan Chen
EGUsphere, https://doi.org/10.22541/au.171053013.30286044/v2, https://doi.org/10.22541/au.171053013.30286044/v2, 2024
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Using Moderate Resolution Imaging Spectroradiometer data products, we quantitatively estimate the resistance and resilience of ecosystem functions to wildfires that occurred in the Columbia River Basin in 2015. Carbon state exhibits lower resistance and resilience than the ecosystem fluxes. The random forest feature importance analysis indicates that burn severity plays a minor role in the resilience of grassland, while it has a relatively major role in the resilience of forest and savanna.
Alexandra Hamm, Erik Schytt Mannerfelt, Aaron A. Mohammed, Scott L. Painter, Ethan T. Coon, and Andrew Frampton
EGUsphere, https://doi.org/10.5194/egusphere-2024-1606, https://doi.org/10.5194/egusphere-2024-1606, 2024
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The fate of thawing permafrost carbon is essential to our understanding of the permafrost-climate feedback and projections of future climate. Here, we modeled the transport of carbon in the groundwater within the active layer. We find that carbon transport velocities and potential microbial mineralization rates are strongly dependent on liquid saturation in the seasonally thawed active layer. In a warming climate, the rate at which permafrost thaws determines how fast carbon can be transported.
Radhakrishna Bangalore Lakshmiprasad, Fan Zhang, Ethan T. Coon, and Thomas Graf
EGUsphere, https://doi.org/10.5194/egusphere-2023-3122, https://doi.org/10.5194/egusphere-2023-3122, 2024
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An effective method of understanding permafrost dynamics due to climate change is numerical modeling. The research work established a novel numerical approach to assess the required level of surface process complexity and set up a numerical model at the Yakou catchment in the Qinghai-Tibet Plateau. The main research findings were that permafrost thawing was not well represented by considering only subsurface processes, and liquid precipitation increased the rate of permafrost degradation.
Stephanie G. Fulton, Morgan Barnes, Mikayla A. Borton, Xingyuan Chen, Yuliya Farris, Brieanne Forbes, Vanessa A. Garayburu-Caruso, Amy E. Goldman, Samantha Grieger, Robert Hall Jr., Matthew H. Kaufman, Xinming Lin, Erin McCann, Sophia A. McKever, Allison Myers-Pigg, Opal C. Otenburg, Aaron C. Pelly, Huiying Ren, Lupita Renteria, Timothy D. Scheibe, Kyongho Son, Jerry Tagestad, Joshua M. Torgeson, and James C. Stegen
EGUsphere, https://doi.org/10.5194/egusphere-2023-3038, https://doi.org/10.5194/egusphere-2023-3038, 2024
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This research examines oxygen use in rivers, which is central to the carbon cycle and water quality. The study focused on an environmentally diverse river basin in the western United States and found that oxygen use in river water was very slow and influenced by factors like water temperature and concentrations of nutrients and carbon in the water. Results suggest that in the study system, most of the oxygen use occurs via mechanisms directly or indirectly associated with riverbed sediments.
Peishi Jiang, Pin Shuai, Alexander Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 27, 2621–2644, https://doi.org/10.5194/hess-27-2621-2023, https://doi.org/10.5194/hess-27-2621-2023, 2023
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We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Utkarsh Mital, Dipankar Dwivedi, James B. Brown, and Carl I. Steefel
Earth Syst. Sci. Data, 14, 4949–4966, https://doi.org/10.5194/essd-14-4949-2022, https://doi.org/10.5194/essd-14-4949-2022, 2022
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We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning framework that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets and constitutes a valuable resource to model the water cycle in the mountains of Colorado, western United States.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
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High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Bo Gao and Ethan T. Coon
The Cryosphere, 16, 4141–4162, https://doi.org/10.5194/tc-16-4141-2022, https://doi.org/10.5194/tc-16-4141-2022, 2022
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Representing water at constant density, neglecting cryosuction, and neglecting heat advection are three commonly applied but not validated simplifications in permafrost models to reduce computation complexity at field scale. We investigated this problem numerically by Advanced Terrestrial Simulator and found that neglecting cryosuction can cause significant bias (10%–60%), constant density primarily affects predicting water saturation, and ignoring heat advection has the least impact.
Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 1727–1743, https://doi.org/10.5194/hess-26-1727-2022, https://doi.org/10.5194/hess-26-1727-2022, 2022
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We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
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It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Yilin Fang, Xingyuan Chen, Jesus Gomez Velez, Xuesong Zhang, Zhuoran Duan, Glenn E. Hammond, Amy E. Goldman, Vanessa A. Garayburu-Caruso, and Emily B. Graham
Geosci. Model Dev., 13, 3553–3569, https://doi.org/10.5194/gmd-13-3553-2020, https://doi.org/10.5194/gmd-13-3553-2020, 2020
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Surface water quality along river corridors can be improved by the area of the stream bed and stream bank in which stream water mixes with shallow groundwater or hyporheic zones (HZs). These zones are ubiquitous and dominated by microorganisms that can process the dissolved nutrients exchanged at this interface of these zones. The modulation of surface water quality can be simulated by connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer representation.
Ahmad Jan, Ethan T. Coon, and Scott L. Painter
Geosci. Model Dev., 13, 2259–2276, https://doi.org/10.5194/gmd-13-2259-2020, https://doi.org/10.5194/gmd-13-2259-2020, 2020
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Computer simulations are important tools for understanding the response of Arctic permafrost to a warming climate. To build confidence in an emerging class of permafrost simulators, we evaluated the Advanced Terrestrial Simulator against field observations from a frozen tundra site near Utqiaġvik (formerly Barrow), Alaska. The 3-year simulations agree well with observations of snow depth, summer water table, soil temperature at multiple locations, and spatially averaged evaporation.
Elchin E. Jafarov, Dylan R. Harp, Ethan T. Coon, Baptiste Dafflon, Anh Phuong Tran, Adam L. Atchley, Youzuo Lin, and Cathy J. Wilson
The Cryosphere, 14, 77–91, https://doi.org/10.5194/tc-14-77-2020, https://doi.org/10.5194/tc-14-77-2020, 2020
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Improved subsurface parameterization and benchmarking data are needed to reduce current uncertainty in predicting permafrost response to a warming climate. We developed a subsurface parameter estimation framework that can be used to estimate soil properties where subsurface data are available. We utilize diverse geophysical datasets such as electrical resistance data, soil moisture data, and soil temperature data to recover soil porosity and soil thermal conductivity.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Han Qiu, Dongwei Gui, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-246, https://doi.org/10.5194/hess-2019-246, 2019
Manuscript not accepted for further review
Gautam Bisht, Maoyi Huang, Tian Zhou, Xingyuan Chen, Heng Dai, Glenn E. Hammond, William J. Riley, Janelle L. Downs, Ying Liu, and John M. Zachara
Geosci. Model Dev., 10, 4539–4562, https://doi.org/10.5194/gmd-10-4539-2017, https://doi.org/10.5194/gmd-10-4539-2017, 2017
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A fully coupled three-dimensional surface and subsurface land model, CP v1.0, was developed to simulate three-way interactions among river water, groundwater, and land surface processes. The coupled model can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.
James C. Stegen, Carolyn G. Anderson, Ben Bond-Lamberty, Alex R. Crump, Xingyuan Chen, and Nancy Hess
Biogeosciences, 14, 4341–4354, https://doi.org/10.5194/bg-14-4341-2017, https://doi.org/10.5194/bg-14-4341-2017, 2017
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CO2 loss from soil to the atmosphere (
soil respiration) is a key ecosystem function, especially in systems with permafrost. We find that soil respiration shows a non-linear threshold at permafrost depths > 140 cm and that the number of large trees governs soil respiration. This suggests that remote sensing could be used to estimate spatial variation in soil respiration and (with knowledge of key thresholds) empirically constrain models that predict ecosystem responses to permafrost thaw.
Hui Wan, Kai Zhang, Philip J. Rasch, Balwinder Singh, Xingyuan Chen, and Jim Edwards
Geosci. Model Dev., 10, 537–552, https://doi.org/10.5194/gmd-10-537-2017, https://doi.org/10.5194/gmd-10-537-2017, 2017
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Solution reproductibility testing is an important task for assuring the software quality of a climate model. A new method is developed using the concept of numerical convergence with respect to temporal resolution. The method is objective, easy to implement, and computationally efficient. This paper describes the new test and demonstrates its utility in the Community Atmosphere Model version 5 (CAM5).
W. J. Riley, F. Maggi, M. Kleber, M. S. Torn, J. Y. Tang, D. Dwivedi, and N. Guerry
Geosci. Model Dev., 7, 1335–1355, https://doi.org/10.5194/gmd-7-1335-2014, https://doi.org/10.5194/gmd-7-1335-2014, 2014
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model
Assessing the value of high-resolution rainfall and streamflow data for hydrological modeling: an analysis based on 63 catchments in southeast China
Catchments do not strictly follow Budyko curves over multiple decades, but deviations are minor and predictable
Scale dependency in modeling nivo-glacial hydrological systems: the case of the Arolla basin, Switzerland
Extended-range forecasting of stream water temperature with deep-learning models
Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell
Projections of streamflow intermittence under climate change in European drying river networks
Economic valuation of subsurface water contributions to watershed ecosystem services using a fully integrated groundwater–surface-water model
Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events
CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland
Runoff component quantification and future streamflow projection in a large mountainous basin based on a multidata-constrained cryospheric–hydrological model
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Simulating the Tone River eastward diversion project in Japan carried out 4 centuries ago
Lack of robustness of hydrological models: a large-sample diagnosis and an attempt to identify hydrological and climatic drivers
Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study
Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation?
State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models
Improving the hydrological consistency of a process-based solute-transport model by simultaneous calibration of streamflow and stream concentrations
Leveraging a time-series event separation method to disentangle time-varying hydrologic controls on streamflow – application to wildfire-affected catchments
The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
Hydrological regime index for non-perennial rivers
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
CONCN: A high-resolution, integrated surface water-groundwater ParFlow modeling platform of continental China
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Technical note: What does the Standardized Streamflow Index actually reflect? Insights and implications for hydrological drought analysis
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Assessing the adequacy of traditional hydrological models for climate change impact studies: A case for long-short-term memory (LSTM) neural networks
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider
Hydrol. Earth Syst. Sci., 29, 1939–1962, https://doi.org/10.5194/hess-29-1939-2025, https://doi.org/10.5194/hess-29-1939-2025, 2025
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Accurate early-warning systems are crucial for reducing the damage caused by flooding events. In this study, we explored the potential of long short-term memory networks for enhancing the forecast accuracy of hydrologic models employed in operational flood forecasting. The presented approach elevated the investigated hydrologic model’s forecast accuracy for further ahead predictions and at flood event runoff.
Mahmut Tudaji, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1919–1937, https://doi.org/10.5194/hess-29-1919-2025, https://doi.org/10.5194/hess-29-1919-2025, 2025
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Common intuition holds that higher input data resolution leads to better results. To assess the benefits of high-resolution data, we conduct simulation experiments using data with various temporal resolutions across multiple catchments and find that higher-resolution data do not always improve model performance, challenging the necessity of pursuing such data. In catchments with small areas or significant flow variability, high-resolution data is more valuable.
Muhammad Ibrahim, Miriam Coenders-Gerrits, Ruud van der Ent, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 1703–1723, https://doi.org/10.5194/hess-29-1703-2025, https://doi.org/10.5194/hess-29-1703-2025, 2025
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The quantification of precipitation into evaporation and runoff is vital for water resources management. The Budyko framework, based on aridity and evaporative indices of a catchment, can be an ideal tool for that. However, recent research highlights deviations of catchments from the expected evaporative index, casting doubt on its reliability. This study quantifies deviations of 2387 catchments, finding them minor and predictable. Integrating these into predictions upholds the framework's efficacy.
Anne-Laure Argentin, Pascal Horton, Bettina Schaefli, Jamal Shokory, Felix Pitscheider, Leona Repnik, Mattia Gianini, Simone Bizzi, Stuart N. Lane, and Francesco Comiti
Hydrol. Earth Syst. Sci., 29, 1725–1748, https://doi.org/10.5194/hess-29-1725-2025, https://doi.org/10.5194/hess-29-1725-2025, 2025
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In this article, we show that by taking the optimal parameters calibrated with a semi-lumped model for the discharge at a catchment's outlet, we can accurately simulate runoff at various points within the study area, including three nested and three neighboring catchments. In addition, we demonstrate that employing more intricate melt models, which better represent physical processes, enhances the transfer of parameters in the simulation, until we observe overparameterization.
Ryan S. Padrón, Massimiliano Zappa, Luzi Bernhard, and Konrad Bogner
Hydrol. Earth Syst. Sci., 29, 1685–1702, https://doi.org/10.5194/hess-29-1685-2025, https://doi.org/10.5194/hess-29-1685-2025, 2025
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We generate operational forecasts of daily maximum stream water temperature for 32 consecutive days at 54 stations in Switzerland with our best-performing data-driven model. The average forecast error is 0.38 °C for 1 d ahead and increases to 0.90 °C for 32 d ahead given the uncertainty in the meteorological variables influencing water temperature. Here we compare the skill of several models, how well they can forecast at new and ungauged stations, and the importance of different model inputs.
Eduardo Acuña Espinoza, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, Ralf Loritz, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1749–1758, https://doi.org/10.5194/hess-29-1749-2025, https://doi.org/10.5194/hess-29-1749-2025, 2025
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Long short-term memory (LSTM) networks have demonstrated state-of-the-art performance for rainfall-runoff hydrological modelling. However, most studies focus on predictions at a daily scale, limiting the benefits of sub-daily (e.g. hourly) predictions in applications like flood forecasting. In this study, we introduce a new architecture, multi-frequency LSTM (MF-LSTM), designed to use inputs of various temporal frequencies to produce sub-daily (e.g. hourly) predictions at a moderate computational cost.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci., 29, 1615–1636, https://doi.org/10.5194/hess-29-1615-2025, https://doi.org/10.5194/hess-29-1615-2025, 2025
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Our study projects how climate change will affect the drying of river segments and stream networks in Europe, using advanced modelling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent and intense and will start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists for evaluating the ecological health of river ecosystem.
Tariq Aziz, Steven K. Frey, David R. Lapen, Susan Preston, Hazen A. J. Russell, Omar Khader, Andre R. Erler, and Edward A. Sudicky
Hydrol. Earth Syst. Sci., 29, 1549–1568, https://doi.org/10.5194/hess-29-1549-2025, https://doi.org/10.5194/hess-29-1549-2025, 2025
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This study determines the value of subsurface water for ecosystem services' supply in an agricultural watershed in Ontario, Canada. Using a fully integrated water model and an economic valuation approach, the research highlights subsurface water's critical role in maintaining watershed ecosystem services. The study informs on the sustainable use of subsurface water and introduces a new method for managing watershed ecosystem services.
Eduardo Acuña Espinoza, Ralf Loritz, Frederik Kratzert, Daniel Klotz, Martin Gauch, Manuel Álvarez Chaves, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 1277–1294, https://doi.org/10.5194/hess-29-1277-2025, https://doi.org/10.5194/hess-29-1277-2025, 2025
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Data-driven techniques have shown the potential to outperform process-based models in rainfall–runoff simulations. Hybrid models, combining both approaches, aim to enhance accuracy and maintain interpretability. Expanding the set of test cases to evaluate hybrid models under different conditions, we test their generalization capabilities for extreme hydrological events.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
Hydrol. Earth Syst. Sci., 29, 1061–1082, https://doi.org/10.5194/hess-29-1061-2025, https://doi.org/10.5194/hess-29-1061-2025, 2025
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This study reconstructs daily runoff in Switzerland (1962–2023) using a deep-learning model, providing a spatially contiguous dataset on a medium-sized catchment grid. The model outperforms traditional hydrological methods, revealing shifts in Swiss water resources, including more frequent dry years and declining summer runoff. The reconstruction is publicly available.
Mengjiao Zhang, Yi Nan, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 29, 1033–1060, https://doi.org/10.5194/hess-29-1033-2025, https://doi.org/10.5194/hess-29-1033-2025, 2025
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Owing to differences in the existing published results, we conducted a detailed analysis of the runoff components and future trends in the Yarlung Tsangpo River basin and found that the contributions of snowmelt and glacier melt runoff to streamflow (both ~5 %) are limited and much lower than previous results. The streamflow in this area will continuously increase in the future, but the overestimated contribution of glacier melt could lead to an underestimation of this increasing trend.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 29, 903–924, https://doi.org/10.5194/hess-29-903-2025, https://doi.org/10.5194/hess-29-903-2025, 2025
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This study develops an integrated framework based on the novel Driving index for changes in Precipitation–Runoff Relationships (DPRR) to explore the controlling changes in precipitation–runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation–runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci., 29, 785–798, https://doi.org/10.5194/hess-29-785-2025, https://doi.org/10.5194/hess-29-785-2025, 2025
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Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine learning (ML) models are increasingly being applied for flood forecasting. Such models are typically trained on large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets that maximise the spatio-temporal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Joško Trošelj and Naota Hanasaki
Hydrol. Earth Syst. Sci., 29, 753–766, https://doi.org/10.5194/hess-29-753-2025, https://doi.org/10.5194/hess-29-753-2025, 2025
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This study presents the first distributed hydrological simulation which confirms claims raised by historians that the eastward diversion project of the Tone River in Japan was conducted 4 centuries ago to increase low flows and subsequent travelling possibilities surrounding the capital, Edo (Tokyo), using inland navigation. We showed that great steps forward can be made for improving quality of life with small human engineering waterworks and small interventions in the regime of natural flows.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 29, 683–700, https://doi.org/10.5194/hess-29-683-2025, https://doi.org/10.5194/hess-29-683-2025, 2025
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This work investigates how hydrological models are transferred to a period in which climate conditions are different to the ones of the period in which they were set up. The robustness assessment test built to detect dependencies between model error and climatic drivers was applied to three hydrological models in 352 catchments in Denmark, France and Sweden. Potential issues are seen in a significant number of catchments for the models, even though the catchments differ for each model.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci., 29, 627–653, https://doi.org/10.5194/hess-29-627-2025, https://doi.org/10.5194/hess-29-627-2025, 2025
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Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci., 29, 447–463, https://doi.org/10.5194/hess-29-447-2025, https://doi.org/10.5194/hess-29-447-2025, 2025
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Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small catchments compared to large catchments, and spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show effects. The results can improve estimations of probabilities of extreme floods.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht H. Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci., 29, 335–360, https://doi.org/10.5194/hess-29-335-2025, https://doi.org/10.5194/hess-29-335-2025, 2025
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Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping to better prepare for and respond to floods.
Jordy Salmon-Monviola, Ophélie Fovet, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 29, 127–158, https://doi.org/10.5194/hess-29-127-2025, https://doi.org/10.5194/hess-29-127-2025, 2025
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To increase the predictive power of hydrological models, it is necessary to improve their consistency, i.e. their physical realism, which is measured by the ability of the model to reproduce observed system dynamics. Using a model to represent the dynamics of water and nitrate and dissolved organic carbon concentrations in an agricultural catchment, we showed that using solute-concentration data for calibration is useful to improve the hydrological consistency of the model.
Haley A. Canham, Belize Lane, Colin B. Phillips, and Brendan P. Murphy
Hydrol. Earth Syst. Sci., 29, 27–43, https://doi.org/10.5194/hess-29-27-2025, https://doi.org/10.5194/hess-29-27-2025, 2025
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The influence of watershed disturbances has proved challenging to disentangle from natural streamflow variability. This study evaluates the influence of time-varying hydrologic controls on rainfall–runoff in undisturbed and wildfire-disturbed watersheds using a novel time-series event separation method. Across watersheds, water year type and season influenced rainfall–runoff patterns. Accounting for these controls enabled clearer isolation of wildfire effects.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci., 28, 5511–5539, https://doi.org/10.5194/hess-28-5511-2024, https://doi.org/10.5194/hess-28-5511-2024, 2024
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Evapotranspiration (ET) is computed from the vegetation (plant transpiration) and soil (soil evaporation). In western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented using the leaf area index (LAI). In this study, we evaluate the importance of the LAI for ET calculation. We take a close look at this interaction and highlight its relevance. Our work contributes to the understanding of terrestrial water cycle processes .
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
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This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
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Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
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Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
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We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
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Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
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The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Pablo Fernando Dornes and Rocío Noelia Comas
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-338, https://doi.org/10.5194/hess-2024-338, 2024
Revised manuscript accepted for HESS
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The Desaguadero-Salado-Chadiluevú-Curacó (DSCC) River is a semiarid river which is severely dammed in its tributaries which collect the snowmelt runoff. This runoff feeds mostly gravitational irrigation systems of very low efficiency. As a result, the DSCC River does not have natural runoff. The proposed Hydrological Regime Index (HRI) is able to discriminate and quantify regime alterations under permanent and non-permanent flow conditions and with low and high impoundment conditions.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
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We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
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Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
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We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Chen Yang, Zitong Jia, Wenjie Xu, Zhongwang Wei, Xiaolang Zhang, Yiguang Zou, Jeffrey McDonnell, Laura Condon, Yongjiu Dai, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-292, https://doi.org/10.5194/hess-2024-292, 2024
Revised manuscript accepted for HESS
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We developed the first high-resolution, integrated surface water-groundwater hydrologic model of the entire continental China using ParFlow. The model shows good performance of streamflow and water table depth when compared to global data products and observations. It is essential for water resources management and decision making in China within a consistent framework in the changing world. It also has significant implications for similar modeling in other places in the world.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
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This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
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This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279, https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript accepted for HESS
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Hydrologic models are needed to provide simulations of water availability, floods and droughts. The accuracy of these simulations is often quantified with so-called performance scores. A common thought is that different models are more or less applicable to different landscapes, depending on how the model works. We show that performance scores are not helpful in distinguishing between different models, and thus cannot easily be used to select an appropriate model for a specific place.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
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A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
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Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
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We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
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Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
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Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Fabián Lema, Pablo A. Mendoza, Nicolás A. Vásquez, Naoki Mizukami, Mauricio Zambrano-Bigiarini, and Ximena Vargas
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-221, https://doi.org/10.5194/hess-2024-221, 2024
Revised manuscript accepted for HESS
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Hydrological droughts affect ecosystems and socioeconomic activities worldwide. Despite they are commonly described with the Standardized Streamflow Index (SSI), there is limited understanding of what it truly reflects in terms of water cycle processes. Here, we used state-of-the-art hydrological models in Andean basins to examine drivers of SSI fluctuations. The results highlight the importance of careful selection of indices and time scales for accurate drought characterization and monitoring.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
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An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
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The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Jean-Luc Martel, François Brissette, Richard Arsenault, Richard Turcotte, Mariana Castañeda-Gonzalez, William Armstrong, Edouard Mailhot, Jasmine Pelletier-Dumont, Gabriel Rondeau-Genesse, and Louis-Philippe Caron
EGUsphere, https://doi.org/10.5194/egusphere-2024-2133, https://doi.org/10.5194/egusphere-2024-2133, 2024
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This study compares Long Short-Term Memory (LSTM) neural networks with traditional hydrological models to predict future streamflow under climate change. Using data from 148 catchments, it finds that LSTM models, which learn from extensive data sequences, perform differently and often better than traditional hydrolgical models. The continental LSTM model, which includes data from diverse climate zones, is particularly effective for understanding climate impacts on water resources.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
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The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
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By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
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Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
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Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
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Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
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Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling, Int. J. Climatol., 33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a
Alemohammad, S. H., McColl, K. A., Konings, A. G., Entekhabi, D., and
Stoffelen, A.: Characterization of precipitation product errors across the
United States using multiplicative triple collocation, Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, 2015. a
Aquanty, I.: HydroGeoSphere User Manual, Waterloo, Ontario, https://www.aquanty.com/hgs-download (last access: 28 April 2022), 2015. a
Behnke, R., Vavrus, S., Allstadt, A., Albright, T., Thogmartin, W. E., and
Radeloff, V. C.: Evaluation of downscaled, gridded climate data for the
conterminous United States, Ecol. Appl., 26, 1338–1351, https://doi.org/10.1002/15-1061, 2016. a, b, c, d
Bolton, D.: The Computation of Equivalent Potential Temperature, Mon. Weather Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2, 1980. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Bruni, G., Reinoso, R., Van De Giesen, N. C., Clemens, F. H., and Ten Veldhuis, J. A.: On the sensitivity of urban hydrodynamic modelling to
rainfall spatial and temporal resolution, Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, 2015. a
Coon, E. T. and Shuai, P.: Watershed Workflow, [Computer Software],
https://doi.org/10.11578/dc.20211008.1, 2021. a
Coon, E. T., Svyatskiy, D., Jan, A., Kikinzon, E., Berndt, M., Atchley, A. L., Harp, D. R., Manzini, G., Shelef, E., Lipnikov, K., Garimella, R., Xu, C., Moulton, J. D., Karra, S., Painter, S. L., Jafarov, E., and Molins, S.:
Advanced Terrestrial Simulator (ATS), US DOE Office of Science (SC),
Biological and Environmental Research (BER), https://doi.org/10.11578/dc.20190911.1,
2019. a, b, c
Coon, E. T., Moulton, J. D., Kikinzon, E., Berndt, M., Manzini, G., Garimella, R., Lipnikov, K., and Painter, S. L.: Coupling surface flow and subsurface flow in complex soil structures using mimetic finite differences, Adv. Water Resour., 144, 103701, https://doi.org/10.1016/j.advwatres.2020.103701, 2020. a
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L.,
Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.: Real-time and
retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 8842, https://doi.org/10.1029/2002jd003118, 2003. a, b
Cromwell, E., Shuai, P., Jiang, P., Coon, E. T., Painter, S. L., Moulton, J. D., Lin, Y., and Chen, X.: Estimating Watershed Subsurface Permeability
From Stream Discharge Data Using Deep Neural Networks, Front. Earth Sci., 9, 1–13, https://doi.org/10.3389/feart.2021.613011, 2021. a
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008. a, b, c, d, e, f, g
Elsner, M. M., Gangopadhyay, S., Pruitt, T., Brekke, L. D., Mizukami, N., and
Clark, M. P.: How Does the Choice of Distributed Meteorological Data Affect
Hydrologic Model Calibration and Streamflow Simulations?, J. Hydrometeorol., 15, 1384–1403, https://doi.org/10.1175/jhm-d-13-083.1, 2014. a, b
Eum, H. I., Dibike, Y., Prowse, T., and Bonsal, B.: Inter-comparison of
high-resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed, Canada, Hydrol. Process., 28, 4250–4271, https://doi.org/10.1002/hyp.10236, 2014. a
Ficchì, A., Perrin, C., and Andréassian, V.: Impact of temporal
resolution of inputs on hydrological model performance: An analysis based on
2400 flood events, J. Hydrol., 538, 454–470, https://doi.org/10.1016/j.jhydrol.2016.04.016, 2016. a
Gao, J., Sheshukov, A. Y., Yen, H., and White, M. J.: Impacts of alternative
climate information on hydrologic processes with SWAT: A comparison of NCDC,
PRISM and NEXRAD datasets, Catena, 156, 353–364, https://doi.org/10.1016/j.catena.2017.04.010, 2017. a
Gatzke, S. E., Beaudette, D. E., Ficklin, D. L., Luo, Y., O'Geen, A. T., and
Zhang, M.: Aggregation Strategies for SSURGO Data: Effects on SWAT Soil
Inputs and Hydrologic Outputs, Soil Sci. Soc. Am. J., 75, 1908–1921, https://doi.org/10.2136/sssaj2010.0418, 2011. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Huscroft, J., Gleeson, T., Hartmann, J., and Börker, J.: Compiling and
Mapping Global Permeability of the Unconsolidated and Consolidated Earth:
GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0), Geophys. Res. Lett., 45, 1897–1904, https://doi.org/10.1002/2017GL075860, 2018. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a, b
Ko, A., Mascaro, G., and Vivoni, E. R.: Strategies to Improve and Evaluate
Physics-Based Hyperresolution Hydrologic Simulations at Regional Basin
Scales, Water Resour. Res., 55, 1129–1152, https://doi.org/10.1029/2018WR023521, 2019. a, b
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow
modeling: A free-surface overland flow boundary condition in a parallel
groundwater flow model, Adv. Water Resour., 29, 945–958,
https://doi.org/10.1016/j.advwatres.2005.08.006, 2006. a
Koppen, W. and Geiger, R.: Handbook of climatology, vol. 1, Gebruder
Borntraeger, Berlin, 1930. a
Kratzert, F., Klotz, D., Hochreiter, S., and Nearing, G. S.: A note on
leveraging synergy in multiple meteorological data sets with deep learning
for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 25, 2685–2703, https://doi.org/10.5194/hess-25-2685-2021, 2021. a
Loheide, S. P. and Lundquist, J. D.: Snowmelt-induced diel fluxes through the
hyporheic zone, Water Resour. Rese., 45, W07404, https://doi.org/10.1029/2008WR007329, 2009. a
Maina, F. Z., Siirila-Woodburn, E. R., and Vahmani, P.: Sensitivity of
meteorological-forcing resolution on hydrologic variables, Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, 2020. a, b, c
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and
transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016. a, b
Mital, U., Dwivedi, D., Brown, J., and Steefel, C.:: Downscaled precipitation and mean air temperature datasets; East-Taylor subbasin; 2008–2019; daily temporal resolution; 400 m spatial resolution, ExaSheds, ESS-DIVE repository [data set], https://doi.org/10.15485/1822259, 2021. a
Mital, U., Dwivedi, D., Brown, J. B., and Steefel, C. I.: Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for East Taylor subbasin (western United States), Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-67, in review, 2022. a
Mitchell, K. E.: The multi-institution North American Land Data Assimilation
System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system, J. Geophys. Res., 109, D07S90, https://doi.org/10.1029/2003JD003823, 2004. a
Mourtzinis, S., Rattalino Edreira, J. I., Conley, S. P., and Grassini, P.: From grid to field: Assessing quality of gridded weather data for agricultural applications, Eur. J. Agron., 82, 163–172, https://doi.org/10.1016/j.eja.2016.10.013, 2017. a
Muche, M. E., Sinnathamby, S., Parmar, R., Knightes, C. D., Johnston, J. M.,
Wolfe, K., Purucker, S. T., Cyterski, M. J., and Smith, D.: Comparison and
Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural
Watershed Using SWAT, J. Am. Water Resour. Assoc., 56, 486–506, https://doi.org/10.1111/1752-1688.12819, 2020. a, b, c, d, e
Ochoa-Rodriguez, S., Wang, L. P., Gires, A., Pina, R. D., Reinoso-Rondinel, R., Bruni, G., Ichiba, A., Gaitan, S., Cristiano, E., Van Assel, J., Kroll, S., Murlà-Tuyls, D., Tisserand, B., Schertzer, D., Tchiguirinskaia, I.,
Onof, C., Willems, P., and Ten Veldhuis, M. C.: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation, J. Hydrol., 531, 389–407, https://doi.org/10.1016/j.jhydrol.2015.05.035, 2015. a, b, c
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z. M., Swenson, S., Thornton, P. E.,
Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S. P., Ricciuto, D. M.,
Sacks, W. J., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), No. NCAR/TN-503+STR, NCAR, p. D6RR1W7M, https://doi.org/10.5065/D6RR1W7M, 2013. a
Pan, M., Sheffield, J., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., and Tarpley, J. D.: Snow process modeling in the North
American Land Data Assimilation System (NLDAS): 1. Evaluation of model simulated snow water equivalent, J. Geophys. Res.-Atmos., 108, 1–14, https://doi.org/10.1029/2003jd003994, 2003. a
Pan, M., Cai, X., Chaney, N. W., Entekhabi, D., and Wood, E. F.: An initial
assessment of SMAP soil moisture retrievals using high-resolution model
simulations and in situ observations, Geophys. Res. Lett., 43, 9662–9668, https://doi.org/10.1002/2016GL069964, 2016. a
Petrone, K., Buffam, I., and Laudon, H.: Hydrologic and biotic control of
nitrogen export during snowmelt: A combined conservative and reactive tracer
approach, Water Resour. Res., 43, 1–13, https://doi.org/10.1029/2006WR005286, 2007. a
Schreiner‐McGraw, A. P. and Ajami, H.: Impact of Uncertainty in Precipitation Forcing Data Sets on the Hydrologic Budget of an Integrated Hydrologic Model in Mountainous Terrain, Water Resour. Res., 56, 1–21, https://doi.org/10.1029/2020WR027639, 2020. a, b
Shangguan, W., Hengl, T., Mendes de Jesus, J., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling, J. Adv. Model. Earth Syst., 9, 65–88, https://doi.org/10.1002/2016MS000686, 2017. a
Sheffield, J., Pan, M., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake,
J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Ramsay, B. H.: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent, J. Geophys. Res.-Atmos., 108, 1–13, https://doi.org/10.1029/2002jd003274, 2003. a
Shuai, P., Cardenas, M. B., Knappett, P. S. K., Bennett, P. C., and Neilson,
B. T.: Denitrification in the banks of fluctuating rivers: The effects of river stage amplitude, sediment hydraulic conductivity and dispersivity, and
ambient groundwater flow, Water Resour. Res., 53, 7951–7967, https://doi.org/10.1002/2017WR020610, 2017. a
Shuai, P., Chen, X., Mital, U., Coon, E., and Dwivedi, D.: Data-model files associated with the manuscript “The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses” (Shuai et al., 2022 HESS), ExaSheds, ESS-DIVE repository [data set], https://doi.org/10.15485/1861432, 2022. a
Song, X., Chen, X., Stegen, J., Hammond, G., Song, H.-S., Dai, H., Graham, E., and Zachara, J. M.: Drought Conditions Maximize the Impact of High-Frequency Flow Variations on Thermal Regimes and Biogeochemical Function in the Hyporheic Zone, Water Resour. Res., 54, 7361–7382,
https://doi.org/10.1029/2018WR022586, 2018. a
Staudinger, M., Stoelzle, M., Cochand, F., Seibert, J., Weiler, M., and
Hunkeler, D.: Your work is my boundary condition!: Challenges and approaches
for a closer collaboration between hydrologists and hydrogeologists, J. Hydrol., 571, 235–243, https://doi.org/10.1016/j.jhydrol.2019.01.058, 2019. a
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001. a
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of
daily meteorological variables over large regions of complex terrain, J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9, 1997. a, b, c, d
Thornton, P. E., Shrestha, R., Thornton, M., Kao, S.-C., Wei, Y., and Wilson,
B. E.: Gridded daily weather data for North America with comprehensive
uncertainty quantification, Scient. Data, 8, 1–17, https://doi.org/10.1038/s41597-021-00973-0, 2021. a, b, c, d
Wetterhall, F., He, Y., Cloke, H., and Pappenberger, F.: Effects of temporal
resolution of input precipitation on the performance of hydrological forecasting, Adv. Geosci., 29, 21–25, https://doi.org/10.5194/adgeo-29-21-2011, 2011. a
Woelber, B., Maneta, M. P., Harper, J., Jencso, K. G., Payton Gardner, W.,
Wilcox, A. C., and López-Moreno, I.: The influence of diurnal snowmelt and transpiration on hillslope throughflow and stream response, Hydrol. Earth Syst. Sci., 22, 4295–4310, https://doi.org/10.5194/hess-22-4295-2018, 2018.
a
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011. a
Xia, Y., Mitchell, K., Ek, M., Cosgrove, B., Sheffield, J., Luo, L., Alonge,
C., Wei, H., Meng, J., Livneh, B., Duan, Q., and Lohmann, D.: Continental-scale water and energy flux analysis and validation for North
American Land Data Assimilation System project phase 2 (NLDAS-2): 2. Validation of model-simulated streamflow, J. Geophys. Res.-Atmos., 117, D03110, https://doi.org/10.1029/2011JD016051, 2012. a, b
Zhang, Y. and Schaap, M. G.: Weighted Recalibration of the Rosetta Pedotransfer Model with Improved Estimates of Hydraulic Parameter Distributions and Summary Statistics (Rosetta3), J. Hydrol., 547, 39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017. a
Short summary
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven...
